The Internet of things has always been one of the most concerned trends in the digital age. This year, the emergence of the COVID-19 pandemic has changed the way we live and work, making the Internet of things a new priority.

As we usher in 2021 and prepare to say goodbye to the turbulent year, let’s look to the future and the IOT trends that will drive future business and technological changes.

In 2021, enterprises will begin to realize that IOT is not only a kind of hype or another catchword, but also a technology with real potential to change the industry. Drawing on insights from multiple industry media and our own in-house business expertise, we identified the following major IOT related trends that we expect to see in 2021.

First, it’s still about the edge

If you dabble in the Internet of things world, you might think, “isn’t that a trend in the past few years?” Well, yes, and for good reason.

Using edge computing, the data is not sent to the centralized data center on the network, but processed and analyzed at the edge of the network, thus reducing the transmission time and delay. In this way, data can be accessed and analyzed in near real time, which is an attractive driving force for many organizations that need to respond quickly. Edge computing also reduces bandwidth costs because information processing is performed at the source, reducing data traffic to centralized data centers.

Although not new, the importance of edge data processing is likely to continue to grow due to the rise of 5g, the increase of globally connected devices and the growth of generated data.

Second, the increase of remote operation cases

In order to cope with the epidemic of covid-19, many manufacturers, distributors, utilities and pharmaceutical companies have to adjust their strategies and quickly track their digital transformation work to adapt to the new regulations that need to work remotely.

Previously unconnected assets are connected to enable remote operations and continue to serve customers. It is expected that by 2021, we will continue to see an increase in the adoption rate of IOT and other technologies for use cases related to remote monitoring of equipment and assets.

Third, predictive (non reactive) maintenance

According to the Vanson Bourne Research Report, 82% of companies have experienced unplanned downtime in the past three years, and the loss caused by downtime can be very high, which is estimated to be equivalent to $250000 per hour.

Historically, maintenance is reactive maintenance for faults. Although it is the cheapest solution initially, it may be the most unfavorable method for finance. Since then, many organizations have adopted preventive maintenance procedures, which rely on scheduled maintenance of equipment.

Predictive maintenance is the latest way to utilize data collected from control systems and connected sensors. With the right data, enterprises can track key indicators of equipment and mechanical wear to predict and prevent unplanned downtime and expensive maintenance costs.

In 2021, we will see a greater shift from strategy to predictive maintenance to improve operations and optimize costs.

Fourth, the Internet of things helps digital twins

The digital twin theory was first conceptualized in 2002. Digital twin is the virtual representation of process, object or system, and its function is the same as the object in real life. We expect that with the growth of interconnected assets, digital twin will also gain traction, visualizing all data points from interconnected sensors in digital twin format, so as to have a more comprehensive understanding of the performance of physical objects and provide insight into potential problems.

Fifth, more advanced data analysis

The real benefits of the Internet of things don’t just come from data, but from smart analysis that can generate useful insights and help businesses make smart decisions.

As the amount of data generated increases, analysis will become more important, and advanced solutions driven by artificial intelligence and machine learning will improve the processing of large and various data structures.

Editor in charge: CC

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